Classification of steel surface defects in steel industry is essential for their detection and also fundamental for the analysis of causes that lead to damages. Timely detection of defects allows to reduce the frequency of their appearance in the final product. This paper considers the classifiers for the recognition of scratches, scrapes and abrasions on metal surfaces. Classifiers are based on the ResNet50 and ResNet152 deep residual neural network architecture. The proposed technique supports the recognition of defects in images and does this with high accuracy. The binary accuracy of the classification based on the test data is 97.14%. The influence of a number of training conditions on the accuracy metrics of the model have been studied. The augmentation conditions have been figured out to make the greatest contribution to improving the accuracy during training. The peculiarities of damages that cause difficulties in their recognition have been studied. The fields of neuron activation have been investigated in the convolutional layers of the model. Feature maps which developed in this case have been found to correspond to the location of the objects of interest. Erroneous cases of the classifier application have been considered. The peculiarities of damages that cause difficulties in their recognition have been studied.

Steel defect diagnostics is important for industry task as it is tied to the product quality and production efficiency. The aim of this paper is evaluating the application of residual neural networks for recognition of industrial steel defects of three classes. Developed and investigated models based on deep residual neural networks for the recognition and classification of surface defects of rolled steel. Investigated the influence of various loss functions, optimizers and hyperparameters on the obtained result and selected optimal model parameters. Based on an ensemble of two deep residual neural networks ResNet50 and ResNet152, a classifier was constructed to detect defects of three classes on flat metal surfaces. The proposed technique allows classifying images with high accuracy. The average binary accuracy of classifying the test data is 96.7% for all images (including defect-free ones). The fields of neuron activation in the convolutional layers of the model were investigated. Feature maps formed in the process were found to reflect the position, size and shape of the objects of interest very well. The proposed ensemble model has proven to be robust and able to accurately recognize steel surface defects. Erroneous recognition cases of the classifier application are investigated. It was shown that errors most often occur in ambiguous situations, where surface artifacts of different types are similar.

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